| E2E | LINODE | |
| GPU CARD | RTX 8000 | RTX 6000 |
| CUDA Cores (Parallel-Processing) | 4608 | 4,608 |
| Tensor Cores (Machine & Deep Learning) | 576 | 576 |
| RT Cores (Ray Tracing) | 72 | 72 |
| GPU Memory | 48 GB GDDR6 | 24 GB GDDR6 |
| Single-Precision Performance | 16.3 TFLOPS | 16.3 TFLOPS |
| Tensor Performance | 130.5 TFLOPS | 130.5 TFLOPS |
| Server configration | GDC.RTX-16.115GB | GPU RTX 6000 |
| vCPU | 16 | 16 |
| RAM | 115 GB | 32 GB |
| SSD | 900 GB | 640 GB |
| GPU CARD | RTX 8000 | RTX 6000 |
| Price | ₹40,000 | ₹72,505 (1000 USD) |
| Server configration | GDC.2xRTX-32.230GB | GPU 2xRTX 6000 |
| vCPU | 32 | 32 |
| RAM | 230 GB | 64 GB |
| SSD | 1800 GB | 1280 GB |
| GPU CARD | 2 x RTX 8000 | 2 x RTX 6000 |
| Price | ₹80,000 | ₹1,44,968 (2000 USD) |
| Server configration | GDC.4xRTX-64.460GB | GPU 4xRTX 6000 |
| vCPU | 64 | 48 |
| RAM | 460 GB | 128 GB |
| SSD | 3600 GB | 2560 GB |
| GPU CARD | 4 x RTX 8000 | 4 x RTX 6000 |
| Price | ₹1,60,000 | ₹2,89,934 (4000 USD) |
Computer vision with image and facial recognition helps quickly identify unlawful entries or persons of interest, resulting in safer communities and a more effective way of deterring crimes.
Enhanced human-computer interaction improves customer satisfaction such as offering products based on customer sentiment analysis in retail outlets or faster banking services with quick authentication based on customer identity and preferences.
With millions of images added every day to media and social channels. The use of computer vision technologies such as metadata extraction and image classification greatly improves efficiency and revenue opportunities.
Using computer vision technologies. Auto manufacturers can provide improved and safer self-driving car navigation realizing the goal of making autonomous driving a reality and a reliable transportation option.
Medical image analysis with computer vision can greatly improve the accuracy and speed of a patient's medical diagnosis, resulting in better treatment outcomes and life expectancy.
Well-trained computer vision incorporated in robotics improves quality assurance and operational efficiencies in manufacturing applications, resulting in more reliable and cost effective products.
Well-trained computer vision incorporated in robotics improves quality assurance and operational efficiencies in manufacturing applications, resulting in more reliable and cost effective products.
Face Recognition algorithms capture more than just features. It is programmed in a manner that enables it to capture all the unique factors of a face and that too from multiple angels. For example, it also measures and memorizes the distance between an individual’s eyes and mouth. Siamese Network in computer vision is used to carry out facial recognition.
Augmented Reality (AR) first takes a real-world environment and then adds a computer-generated input to it. Several parts from both of the environments, real and augmented, can interact together and even digitally manipulated. Therefore, AR can simply be defined as an amalgamation of both of the worlds. It renders a 3D registration of real and virtual objects.
Visual search uses images as keywords as opposed to texts and searches for related images, websites, blogs, or any other posts. Visual Search Engine is programmed in a manner that bridges the time gap between your search. For example, Google Lens allows the users to look for objects through the lens and get similar results as per their image search.
It is of no surprise that multiple algorithms exist in the computer vision field to detect human gestures and postures. They can interpret human gestures originated from any motion or state of the human body. For example, a store supervisor can carry out emotion recognition to determine if customers visiting the store are happy with the services or not.
Computer Vision also finds a wide range of applications in the healthcare sector. It can assist medical professionals in training. Doctors can interpret medical images used in techniques like X-Ray and MRI using computer vision efficiently.
E2E Networks helps in Accelerate Convolutional Neural Networks based deep-learning workloads like video analysis, facial recognition, medical imaging and others.
is suitable for a wide range of uses
Train complex models at high speed to improve predictions and decisions of your algorithms. Use any framework or library: TensorFlow, PyTorch, Caffe, MXNet, Auto-Keras, and many more.
Accelerate Convolutional Neural Networks based deep-learning workloads like video analysis, facial recognition, medical imaging and others
Analyze and calculate large and complex financial data; performtons of transactions in real-time. Do accurate financial forecasting, faster
Design and implement data-parallel algorithms that scale to hundreds of tightly coupled processing units: molecular modelling, fluid dynamics and others
Deal with large-size data sets and continuously growing data, splitting it up between processors to crunch through voluminous data sets at a quicker rate
We at CamCom are using E2E GPU servers for a while now and the price-performance is the best in the Indian market. We also have enjoyed a fast turnaround from the support and sales team always. I highly recommend the E2E GPU servers for machine learning, deep learning and Image processing purpose